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Python: simple Agent sample (#180)
* tweaks to agents and sample * updated clients and agents * single line run and print * improved tool handling * added note on setting max iterations * fixed streaming param name * updated tools test * made kwargs alphabetical * added params to run methods * tweak to ensure right overload
This commit is contained in:
Eduard van Valkenburg
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GitHub
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GitHub
parent
59bf7343af
commit
62917ee5e5
@@ -2,11 +2,11 @@
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import asyncio
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from abc import ABC, abstractmethod
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from collections.abc import AsyncIterable, Awaitable, Callable, MutableSequence, Sequence
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from collections.abc import AsyncIterable, Awaitable, Callable, MutableMapping, MutableSequence, Sequence
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from functools import wraps
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from typing import Annotated, Any, Generic, Literal, Protocol, TypeVar, runtime_checkable
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from pydantic import BaseModel, PrivateAttr, StringConstraints
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from pydantic import BaseModel, StringConstraints
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from ._logging import get_logger
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from ._pydantic import AFBaseModel
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@@ -37,24 +37,6 @@ logger = get_logger()
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# region: Tool Calling Functions and Decorators
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def _merge_function_results(
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messages: list[ChatMessage],
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) -> ChatMessage:
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"""Combine multiple function result content types to one chat message content type.
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This method combines the FunctionResultContent items from separate ChatMessageContent messages,
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and is used in the event that the `context.terminate = True` condition is met.
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"""
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contents: list[Any] = []
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for message in messages:
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contents.extend([item for item in message.contents if isinstance(item, FunctionResultContent)])
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return ChatMessage(
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role="tool",
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contents=contents,
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)
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async def _auto_invoke_function(
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function_call_content: FunctionCallContent,
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custom_args: dict[str, Any] | None = None,
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@@ -106,7 +88,7 @@ def _prepare_tools_and_tool_choice(chat_options: ChatOptions) -> None:
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chat_options.tool_choice = ChatToolMode.NONE.mode
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return
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chat_options.tools = [
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(_tool_to_json_schema_spec(t) if isinstance(t, AITool) else t) for t in chat_options.tools or []
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(_tool_to_json_schema_spec(t) if isinstance(t, AITool) else t) for t in chat_options._ai_tools or []
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]
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if not chat_options.tools:
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chat_options.tool_choice = ChatToolMode.NONE.mode
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@@ -115,11 +97,7 @@ def _prepare_tools_and_tool_choice(chat_options: ChatOptions) -> None:
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def _tool_call_non_streaming(func: TInnerGetResponse) -> TInnerGetResponse:
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"""Decorate the internal _inner_get_response method to enable tool calls.
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Remarks:
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Relies on a class that has the _tool_map attribute for the executable tools to call.
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"""
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"""Decorate the internal _inner_get_response method to enable tool calls."""
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@wraps(func)
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async def wrapper(
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@@ -131,7 +109,7 @@ def _tool_call_non_streaming(func: TInnerGetResponse) -> TInnerGetResponse:
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) -> ChatResponse:
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response: ChatResponse | None = None
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fcc_messages: list[ChatMessage] = []
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for attempt_idx in range(self.maximum_iterations_per_request):
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for attempt_idx in range(getattr(self, "__maximum_iterations_per_request", 10)):
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response = await func(self, messages=messages, chat_options=chat_options)
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# if there are function calls, we will handle them first
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function_calls = [it for it in response.messages[0].contents if isinstance(it, FunctionCallContent)]
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@@ -141,7 +119,7 @@ def _tool_call_non_streaming(func: TInnerGetResponse) -> TInnerGetResponse:
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_auto_invoke_function(
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function_call,
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custom_args=kwargs,
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tool_map=self._tool_map,
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tool_map={t.name: t for t in chat_options._ai_tools or [] if isinstance(t, AIFunction)},
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sequence_index=seq_idx,
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request_index=attempt_idx,
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)
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@@ -181,11 +159,7 @@ def _tool_call_non_streaming(func: TInnerGetResponse) -> TInnerGetResponse:
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def _tool_call_streaming(func: TInnerGetStreamingResponse) -> TInnerGetStreamingResponse:
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"""Decorate the internal _inner_get_response method to enable tool calls.
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Remarks:
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Relies on a class that has the _tool_map attribute for the executable tools to call.
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"""
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"""Decorate the internal _inner_get_response method to enable tool calls."""
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@wraps(func)
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async def wrapper(
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@@ -195,7 +169,8 @@ def _tool_call_streaming(func: TInnerGetStreamingResponse) -> TInnerGetStreaming
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chat_options: ChatOptions,
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**kwargs: Any,
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) -> AsyncIterable[ChatResponseUpdate]:
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for attempt_idx in range(self.maximum_iterations_per_request):
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"""Wrap the inner get streaming response method to handle tool calls."""
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for attempt_idx in range(getattr(self, "__maximum_iterations_per_request", 10)):
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function_call_returned = False
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all_messages: list[ChatResponseUpdate] = []
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async for update in func(self, messages=messages, chat_options=chat_options):
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@@ -221,7 +196,7 @@ def _tool_call_streaming(func: TInnerGetStreamingResponse) -> TInnerGetStreaming
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_auto_invoke_function(
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function_call,
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custom_args=kwargs,
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tool_map=self._tool_map,
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tool_map={t.name: t for t in chat_options._ai_tools or [] if isinstance(t, AIFunction)},
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sequence_index=seq_idx,
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request_index=attempt_idx,
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)
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@@ -247,10 +222,25 @@ def use_tool_calling(cls: type[TChatClientBase]) -> type[TChatClientBase]:
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Remarks:
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This only works on classes that derive from ChatClientBase
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and have the _tool_map attribute as well as the _inner_get_response
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and the _inner_get_response
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and _inner_get_streaming_response methods.
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It also sets a __maximum_iterations_per_request attribute on the class.
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if you want to expose this to end_users, do a version of this:
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```python
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@use_tool_calling
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class MyChatClient(ChatClientBase):
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@property
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def maximum_iterations_per_request(self):
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return getattr(self, "__maximum_iterations_per_request", 10)
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@maximum_iterations_per_request.setter
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def maximum_iterations_per_request(self, value: int) -> None:
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setattr(self, "__maximum_iterations_per_request", value)
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```
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"""
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setattr(cls, "__maximum_iterations_per_request", 10)
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if inner_response := getattr(cls, "_inner_get_response", None):
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cls._inner_get_response = _tool_call_non_streaming(inner_response) # type: ignore
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if inner_streaming_response := getattr(cls, "_inner_get_streaming_response", None):
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@@ -267,15 +257,54 @@ class ChatClient(Protocol):
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async def get_response(
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self,
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messages: str | ChatMessage | list[ChatMessage],
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messages: str | ChatMessage | list[str] | list[ChatMessage],
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*,
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frequency_penalty: float | None = None,
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logit_bias: dict[str | int, float] | None = None,
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max_tokens: int | None = None,
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metadata: dict[str, Any] | None = None,
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model: str | None = None,
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presence_penalty: float | None = None,
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response_format: type[BaseModel] | None = None,
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seed: int | None = None,
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stop: str | Sequence[str] | None = None,
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store: bool | None = None,
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temperature: float | None = None,
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tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
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tools: AITool
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| list[AITool]
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| Callable[..., Any]
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| list[Callable[..., Any]]
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| MutableMapping[str, Any]
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| list[MutableMapping[str, Any]]
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| None = None,
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top_p: float | None = None,
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user: str | None = None,
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additional_properties: dict[str, Any] | None = None,
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**kwargs: Any,
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) -> ChatResponse:
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"""Sends input and returns the response.
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Args:
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messages: The sequence of input messages to send.
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**kwargs: Additional options for the request, such as ai_model_id, temperature, etc.
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See `ChatOptions` for more details.
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frequency_penalty: the frequency penalty to use.
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logit_bias: the logit bias to use.
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max_tokens: The maximum number of tokens to generate.
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metadata: additional metadata to include in the request.
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model: The model to use for the agent.
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presence_penalty: the presence penalty to use.
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response_format: the format of the response.
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seed: the random seed to use.
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stop: the stop sequence(s) for the request.
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store: whether to store the response.
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temperature: the sampling temperature to use.
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tool_choice: the tool choice for the request.
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tools: the tools to use for the request.
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top_p: the nucleus sampling probability to use.
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user: the user to associate with the request.
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additional_properties: additional properties to include in the request
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kwargs: any additional keyword arguments,
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will only be passed to functions that are called.
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Returns:
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The response messages generated by the client.
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@@ -287,15 +316,54 @@ class ChatClient(Protocol):
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def get_streaming_response(
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self,
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messages: str | ChatMessage | list[ChatMessage],
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messages: str | ChatMessage | list[str] | list[ChatMessage],
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*,
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frequency_penalty: float | None = None,
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logit_bias: dict[str | int, float] | None = None,
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max_tokens: int | None = None,
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metadata: dict[str, Any] | None = None,
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model: str | None = None,
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presence_penalty: float | None = None,
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response_format: type[BaseModel] | None = None,
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seed: int | None = None,
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stop: str | Sequence[str] | None = None,
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store: bool | None = None,
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temperature: float | None = None,
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tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
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tools: AITool
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| list[AITool]
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| Callable[..., Any]
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| list[Callable[..., Any]]
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| MutableMapping[str, Any]
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| list[MutableMapping[str, Any]]
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| None = None,
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top_p: float | None = None,
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user: str | None = None,
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additional_properties: dict[str, Any] | None = None,
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**kwargs: Any,
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) -> AsyncIterable[ChatResponseUpdate]:
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"""Sends input messages and streams the response.
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Args:
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messages: The sequence of input messages to send.
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**kwargs: Additional options for the request, such as ai_model_id, temperature, etc.
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See `ChatOptions` for more details.
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frequency_penalty: the frequency penalty to use.
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logit_bias: the logit bias to use.
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max_tokens: The maximum number of tokens to generate.
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metadata: additional metadata to include in the request.
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model: The model to use for the agent.
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presence_penalty: the presence penalty to use.
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response_format: the format of the response.
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seed: the random seed to use.
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stop: the stop sequence(s) for the request.
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store: whether to store the response.
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temperature: the sampling temperature to use.
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tool_choice: the tool choice for the request.
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tools: the tools to use for the request.
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top_p: the nucleus sampling probability to use.
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user: the user to associate with the request.
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additional_properties: additional properties to include in the request
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kwargs: any additional keyword arguments,
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will only be passed to functions that are called.
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Yields:
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An async iterable of chat response updates containing the content of the response messages
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@@ -311,19 +379,21 @@ class ChatClientBase(AFBaseModel, ABC):
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"""Base class for chat clients."""
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ai_model_id: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1)]
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maximum_iterations_per_request: int = 10
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_tool_map: dict[str, AIFunction[BaseModel, Any]] = PrivateAttr(default_factory=dict) # type: ignore
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def _prepare_messages(self, messages: str | ChatMessage | list[str | ChatMessage]) -> MutableSequence[ChatMessage]:
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def _prepare_messages(
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self, messages: str | ChatMessage | list[str] | list[ChatMessage]
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) -> MutableSequence[ChatMessage]:
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"""Turn the allowed input into a list of chat messages."""
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if isinstance(messages, str):
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messages = [ChatMessage(role="user", text=messages)]
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return [ChatMessage(role="user", text=messages)]
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if isinstance(messages, ChatMessage):
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messages = [messages]
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for i, msg in enumerate(messages):
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return [messages]
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return_messages: list[ChatMessage] = []
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for msg in messages:
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if isinstance(msg, str):
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messages[i] = ChatMessage(role="user", text=msg)
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return messages # type: ignore[return-value]
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msg = ChatMessage(role="user", text=msg)
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return_messages.append(msg)
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return return_messages
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# region Internal methods to be implemented by the derived classes
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@@ -377,23 +447,29 @@ class ChatClientBase(AFBaseModel, ABC):
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async def get_response(
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self,
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messages: str | ChatMessage | list[str | ChatMessage],
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messages: str | ChatMessage | list[str] | list[ChatMessage],
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*,
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model: str | None = None,
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max_tokens: int | None = None,
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temperature: float | None = None,
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top_p: float | None = None,
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tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
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tools: AITool | Sequence[AITool] | None = None,
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response_format: type[BaseModel] | None = None,
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user: str | None = None,
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stop: str | Sequence[str] | None = None,
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frequency_penalty: float | None = None,
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logit_bias: dict[str | int, float] | None = None,
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presence_penalty: float | None = None,
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seed: int | None = None,
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store: bool | None = None,
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max_tokens: int | None = None,
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metadata: dict[str, Any] | None = None,
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model: str | None = None,
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presence_penalty: float | None = None,
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response_format: type[BaseModel] | None = None,
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seed: int | None = None,
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stop: str | Sequence[str] | None = None,
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store: bool | None = None,
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temperature: float | None = None,
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tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
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tools: AITool
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| list[AITool]
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| Callable[..., Any]
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| list[Callable[..., Any]]
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| MutableMapping[str, Any]
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| list[MutableMapping[str, Any]]
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| None = None,
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top_p: float | None = None,
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user: str | None = None,
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additional_properties: dict[str, Any] | None = None,
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**kwargs: Any,
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) -> ChatResponse:
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@@ -401,73 +477,80 @@ class ChatClientBase(AFBaseModel, ABC):
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Args:
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messages: the message or messages to send to the model
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model: the model to use for the request
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max_tokens: the maximum number of tokens to generate
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temperature: the sampling temperature to use
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top_p: the nucleus sampling probability to use
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tool_choice: the tool choice for the request
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tools: the tools to use for the request
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response_format: the format of the response
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user: the user to associate with the request
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stop: the stop sequence(s) for the request
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frequency_penalty: the frequency penalty to use
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logit_bias: the logit bias to use
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presence_penalty: the presence penalty to use
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seed: the random seed to use
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store: whether to store the response
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metadata: additional metadata to include in the request
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additional_properties: additional properties to include in the request
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frequency_penalty: the frequency penalty to use.
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logit_bias: the logit bias to use.
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max_tokens: The maximum number of tokens to generate.
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metadata: additional metadata to include in the request.
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model: The model to use for the agent.
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presence_penalty: the presence penalty to use.
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response_format: the format of the response.
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seed: the random seed to use.
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stop: the stop sequence(s) for the request.
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store: whether to store the response.
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temperature: the sampling temperature to use.
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tool_choice: the tool choice for the request.
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tools: the tools to use for the request.
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top_p: the nucleus sampling probability to use.
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user: the user to associate with the request.
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additional_properties: additional properties to include in the request.
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kwargs: any additional keyword arguments,
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will only be passed to functions that are called.
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Returns:
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A chat response from the model.
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"""
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if tools is not None:
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if not isinstance(tools, Sequence):
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tools = [tools]
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self._tool_map = {tool.name: tool for tool in tools if isinstance(tool, AIFunction)}
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chat_options = ChatOptions(
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ai_model_id=model,
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max_tokens=max_tokens,
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temperature=temperature,
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top_p=top_p,
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tool_choice=tool_choice,
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tools=tools,
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response_format=response_format,
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user=user,
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stop=stop,
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frequency_penalty=frequency_penalty,
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logit_bias=logit_bias,
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presence_penalty=presence_penalty,
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seed=seed,
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store=store,
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metadata=metadata,
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additional_properties=additional_properties or {},
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)
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if "chat_options" in kwargs:
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chat_options = kwargs.pop("chat_options")
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if not isinstance(chat_options, ChatOptions):
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raise TypeError("chat_options must be an instance of ChatOptions")
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else:
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chat_options = ChatOptions(
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ai_model_id=model,
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frequency_penalty=frequency_penalty,
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logit_bias=logit_bias,
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max_tokens=max_tokens,
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metadata=metadata,
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presence_penalty=presence_penalty,
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response_format=response_format,
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seed=seed,
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stop=stop,
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store=store,
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temperature=temperature,
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top_p=top_p,
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tool_choice=tool_choice,
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tools=tools, # type: ignore
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user=user,
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additional_properties=additional_properties or {},
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)
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prepped_messages = self._prepare_messages(messages)
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_prepare_tools_and_tool_choice(chat_options=chat_options)
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return await self._inner_get_response(messages=prepped_messages, chat_options=chat_options, **kwargs)
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async def get_streaming_response(
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self,
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messages: str | ChatMessage | list[str | ChatMessage],
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messages: str | ChatMessage | list[str] | list[ChatMessage],
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*,
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model: str | None = None,
|
||||
max_tokens: int | None = None,
|
||||
temperature: float | None = None,
|
||||
top_p: float | None = None,
|
||||
tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
|
||||
tools: AITool | Sequence[AITool] | None = None,
|
||||
response_format: type[BaseModel] | None = None,
|
||||
user: str | None = None,
|
||||
stop: str | Sequence[str] | None = None,
|
||||
frequency_penalty: float | None = None,
|
||||
logit_bias: dict[str | int, float] | None = None,
|
||||
presence_penalty: float | None = None,
|
||||
seed: int | None = None,
|
||||
store: bool | None = None,
|
||||
max_tokens: int | None = None,
|
||||
metadata: dict[str, Any] | None = None,
|
||||
model: str | None = None,
|
||||
presence_penalty: float | None = None,
|
||||
response_format: type[BaseModel] | None = None,
|
||||
seed: int | None = None,
|
||||
stop: str | Sequence[str] | None = None,
|
||||
store: bool | None = None,
|
||||
temperature: float | None = None,
|
||||
tool_choice: ChatToolMode | Literal["auto", "required", "none"] | dict[str, Any] | None = "auto",
|
||||
tools: AITool
|
||||
| list[AITool]
|
||||
| Callable[..., Any]
|
||||
| list[Callable[..., Any]]
|
||||
| MutableMapping[str, Any]
|
||||
| list[MutableMapping[str, Any]]
|
||||
| None = None,
|
||||
top_p: float | None = None,
|
||||
user: str | None = None,
|
||||
additional_properties: dict[str, Any] | None = None,
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterable[ChatResponseUpdate]:
|
||||
@@ -475,50 +558,51 @@ class ChatClientBase(AFBaseModel, ABC):
|
||||
|
||||
Args:
|
||||
messages: the message or messages to send to the model
|
||||
model: the model to use for the request
|
||||
max_tokens: the maximum number of tokens to generate
|
||||
temperature: the sampling temperature to use
|
||||
top_p: the nucleus sampling probability to use
|
||||
tool_choice: the tool choice for the request
|
||||
tools: the tools to use for the request
|
||||
response_format: the format of the response
|
||||
user: the user to associate with the request
|
||||
stop: the stop sequence(s) for the request
|
||||
frequency_penalty: the frequency penalty to use
|
||||
logit_bias: the logit bias to use
|
||||
presence_penalty: the presence penalty to use
|
||||
seed: the random seed to use
|
||||
store: whether to store the response
|
||||
metadata: additional metadata to include in the request
|
||||
max_tokens: The maximum number of tokens to generate.
|
||||
metadata: additional metadata to include in the request.
|
||||
model: The model to use for the agent.
|
||||
presence_penalty: the presence penalty to use.
|
||||
response_format: the format of the response.
|
||||
seed: the random seed to use.
|
||||
stop: the stop sequence(s) for the request.
|
||||
store: whether to store the response.
|
||||
temperature: the sampling temperature to use.
|
||||
tool_choice: the tool choice for the request.
|
||||
tools: the tools to use for the request.
|
||||
top_p: the nucleus sampling probability to use.
|
||||
user: the user to associate with the request.
|
||||
additional_properties: additional properties to include in the request
|
||||
kwargs: any additional keyword arguments
|
||||
|
||||
Yields:
|
||||
A stream representing the response(s) from the LLM.
|
||||
"""
|
||||
if tools is not None:
|
||||
if not isinstance(tools, Sequence):
|
||||
tools = [tools]
|
||||
self._tool_map = {tool.name: tool for tool in tools if isinstance(tool, AIFunction)}
|
||||
chat_options = ChatOptions(
|
||||
ai_model_id=model,
|
||||
max_tokens=max_tokens,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
tool_choice=tool_choice,
|
||||
tools=tools,
|
||||
response_format=response_format,
|
||||
user=user,
|
||||
stop=stop,
|
||||
frequency_penalty=frequency_penalty,
|
||||
logit_bias=logit_bias,
|
||||
presence_penalty=presence_penalty,
|
||||
seed=seed,
|
||||
store=store,
|
||||
metadata=metadata,
|
||||
additional_properties=additional_properties or {},
|
||||
**kwargs,
|
||||
)
|
||||
if "chat_options" in kwargs:
|
||||
chat_options = kwargs.pop("chat_options")
|
||||
if not isinstance(chat_options, ChatOptions):
|
||||
raise TypeError("chat_options must be an instance of ChatOptions")
|
||||
else:
|
||||
chat_options = ChatOptions(
|
||||
ai_model_id=model,
|
||||
frequency_penalty=frequency_penalty,
|
||||
logit_bias=logit_bias,
|
||||
max_tokens=max_tokens,
|
||||
metadata=metadata,
|
||||
presence_penalty=presence_penalty,
|
||||
response_format=response_format,
|
||||
seed=seed,
|
||||
stop=stop,
|
||||
store=store,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
tool_choice=tool_choice,
|
||||
tools=tools, # type: ignore
|
||||
user=user,
|
||||
additional_properties=additional_properties or {},
|
||||
**kwargs,
|
||||
)
|
||||
prepped_messages = self._prepare_messages(messages)
|
||||
_prepare_tools_and_tool_choice(chat_options=chat_options)
|
||||
async for update in self._inner_get_streaming_response(
|
||||
|
||||
Reference in New Issue
Block a user